The explosion of popularity in social media, such as internet forums, weblogs (blogs), wikis, etc., in the past decade has created a new opportunity to measure public opinion, attitude, and social structures. A very common social structure investigated is online communities, or groups. There are a number of motivations for studying online groups and communities; increasing online community involvement; recommender systems; collaborative filtering; and identifying authoritative or influential sources.
Methods described to identify online communities tend to rely on varying methods for link analysis. In other words, an online community is commonly defined as the amount of online interconnectedness of entities. While these approaches have been shown to be effective in some contexts, relying on interconnectedness for identifying a community can miss many potential opportunities to identify groups of people that are very similar to one another, but may never actually interact online.
While the traditional definition of groups can include face-to-face interaction between entities, it is well recognized that online groups need not comprise entities who have met face-to-face, but rather who have interacted in some manner (for example, commented on a blog post, emailed, etc.). However, there can be great value in identifying abstract groups. An abstract group, as used herein, can refer to a group in which the members need not interact explicitly, but must demonstrate cohesiveness in some way. In fact, the whole notion of compiling a focus group in marketing is based on the premise that one can make generalities about abstract groups: Marketers target demographic groups, for instance, females in the 18-25 age range. Abstract online groups go beyond demographics. For example, on LIVEJOURNAL, there are a number of categories (e.g., gaming) by which one can categorize himself and/or his blog. While a number of the entities that self select a particular category might interact, there is no explicit requirement that they do so. If one is interested in marketing to a gaming crowd, for instance, knowing all persons interested in gaming would be useful, even if they do not interact directly with one another.
Link type analyses are virtually ineffective at identifying abstract groups since the members are not necessarily connected. Accordingly, other methods are required to identify abstract groups, especially methods that do not rely solely on link analysis techniques.